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甲醇是一种兼具新能源潜力与基础化工价值的重要化学品，主要通过罐区储存。甲醇泄漏极易引发火灾，且燃烧时烟气较少，以气体辐射为主要传热方式，导致火灾识别预警困难，引发多米诺效应事故。采用事故树分析，辨识甲醇罐区主要风险因素，基于贝叶斯网络理论建立罐区火灾事故概率模型，量化事故概率及识别关键致灾因素；通过动态贝叶斯网络构建火灾多米诺效应演化模型，实现对甲醇罐区火灾及次生灾害风险的动态表征。结果表明，车辆排烟筒火星、违章吸烟火星及动火后留火等是导致甲醇罐区火灾事故的关键因素，且随时间推移，多米诺事故发生概率呈上升趋势。研究可为甲醇罐区火灾防控策略提供依据，助力“双碳”目标实现。
Abstract：Methanol is an important chemical with both new energy potential and fundamental chemical value, primarily stored in tank farms. Methanol leaks can easily lead to fires, and during combustion, they produce minimal smoke, with gas radiation serving as the primary heat transfer mechanism. This characteristic complicates fire detection and warning, potentially resulting in domino-effect accidents. This study systematically identifies risks in the methanol tank area through fault tree analysis, utilizing vertical storage tanks and pipeline systems as analysis nodes to pinpoint key risk factors. Based on Bayesian network theory, a probability model for tank farm fire accidents was constructed to enable quantitative analysis of accident probabilities. A dynamic Bayesian network method was employed to establish a fire domino effect evolution model, revealing the chain propagation mechanism by which initial accidents trigger secondary incidents. The study identified 56 risk events within the methanol tank farm, including 1 leaf node(T), 21 intermediate nodes(M), and 34 root nodes(X). The calculated probability of a fire accident occurring in the methanol tank area is 2.34×10-6, with the probability of an ignition source being 1.75×10-2 and the probability of a methanol leak at 2.47×10-2. Through Bayesian network sensitivity analysis, the key factors contributing to fire accidents were found to be sparks from vehicle exhaust pipes, illegal smoking mars, and residual fires following hot work. The dynamic Bayesian network model illustrated the domino effect of accidents, simulating the failure process associated with tank area fires. It was observed that the failure probability of each storage tank increased continuously over 1 000 seconds, peaking at 4.22×10-2 from an initial value of 2.34×10-6. The constructed Bayesian network model effectively identifies the key contributing factors of pool fires in methanol tank farms and their potential domino effects, providing a solid foundation for developing fire prevention and control strategies in these facilities.
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基本信息:
DOI：10.13637/j.issn.1009-6094.2025.1223
中图分类号:TQ223.121;TQ086;TP181
引用信息:
[1]陈超,袁博渊,王海军,等.基于贝叶斯网络的甲醇罐区火灾事故风险分析[J].安全与环境学报,2026,26(07):2499-2505.DOI:10.13637/j.issn.1009-6094.2025.1223.
基金信息:
四川省国际科技创新合作/港澳台科技创新合作项目(2025YFHZ0176); 四川省科技计划项目(2023YFS0412)
2026-03-27
2026-03-27
2026-03-27